{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "针对准确率不足的问题，优化思路：\n",
    "1:提高每次训练的batchSize\n",
    "2:增加神经元层数\n",
    "3:使用Adam优化器\n",
    "4:增加训练次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Extracting ./mnist/train-images-idx3-ubyte.gz\nExtracting ./mnist/train-labels-idx1-ubyte.gz\nExtracting ./mnist/t10k-images-idx3-ubyte.gz\nExtracting ./mnist/t10k-labels-idx1-ubyte.gz\nafter 500 training steps, the loss is 0.124036, the validation accuracy is 0.9644\nafter 1000 training steps, the loss is 0.0378139, the validation accuracy is 0.977\nafter 1500 training steps, the loss is 0.0208725, the validation accuracy is 0.9798\nafter 2000 training steps, the loss is 0.00930801, the validation accuracy is 0.981\nafter 2500 training steps, the loss is 0.00484432, the validation accuracy is 0.9782\nafter 3000 training steps, the loss is 0.0102528, the validation accuracy is 0.979\nafter 3500 training steps, the loss is 0.00285577, the validation accuracy is 0.9816\nafter 4000 training steps, the loss is 0.00131344, the validation accuracy is 0.9802\nafter 4500 training steps, the loss is 0.0139569, the validation accuracy is 0.9796\nafter 5000 training steps, the loss is 0.0149896, the validation accuracy is 0.9764\nafter 5500 training steps, the loss is 0.0226488, the validation accuracy is 0.977\nafter 6000 training steps, the loss is 0.00342969, the validation accuracy is 0.9842\nafter 6500 training steps, the loss is 0.0358363, the validation accuracy is 0.9806\nafter 7000 training steps, the loss is 0.00155462, the validation accuracy is 0.9814\nafter 7500 training steps, the loss is 0.000296742, the validation accuracy is 0.982\nafter 8000 training steps, the loss is 0.0208584, the validation accuracy is 0.9804\nafter 8500 training steps, the loss is 0.000331399, the validation accuracy is 0.9836\nafter 9000 training steps, the loss is 3.69697e-05, the validation accuracy is 0.9838\nafter 9500 training steps, the loss is 2.54402e-05, the validation accuracy is 0.9836\nthe training is finish!\nthe test accuarcy is: 0.9843\n"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data \n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "tf.logging.set_verbosity(tf.logging.INFO)\n",
    "mnist = input_data.read_data_sets(\"./mnist\")\n",
    "\n",
    "x = tf.placeholder(\"float\", [None, 784])\n",
    "y = tf.placeholder(\"int64\", [None])\n",
    "learning_rate = tf.placeholder(\"float\")\n",
    "epsilon = tf.placeholder(\"float\")\n",
    "def initialize(shape, stddev=0.1):\n",
    "  return tf.truncated_normal(shape, stddev=0.1)\n",
    "\n",
    "# 增加隐层，提高学习度，逼近决策边界\n",
    "\n",
    "W = tf.Variable(initialize([784, 300]))\n",
    "b = tf.Variable(initialize([300]))\n",
    "output = tf.nn.relu(tf.matmul(x, W) + b)\n",
    "\n",
    "L1_units_count = 100\n",
    "\n",
    "W_1 = tf.Variable(initialize([300, L1_units_count]))\n",
    "b_1 = tf.Variable(initialize([L1_units_count]))\n",
    "logits_1 = tf.matmul(output, W_1) + b_1\n",
    "output_1 = tf.nn.relu(logits_1)\n",
    "\n",
    "L2_units_count = 10 \n",
    "W_2 = tf.Variable(initialize([L1_units_count, L2_units_count]))\n",
    "b_2 = tf.Variable(initialize([L2_units_count]))\n",
    "logits_2 = tf.matmul(output_1, W_2) + b_2  \n",
    "\n",
    "logits = logits_2\n",
    "\n",
    "cross_entropy_loss = tf.reduce_mean(\n",
    "    tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "\n",
    "# 使用RMSPropOptimizer 优化器\n",
    "optimizer = tf.train.AdamOptimizer(\n",
    "    learning_rate=learning_rate).minimize(cross_entropy_loss)\n",
    "\n",
    "pred = tf.nn.softmax(logits)\n",
    "correct_pred = tf.equal(tf.argmax(pred, 1), y)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "# batch_sise 增大到100，训练的正确率略有提高，但是测试集没什么变化\n",
    "batch_size = 200\n",
    "trainig_step = 10000\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    #定义验证集与测试集\n",
    "    validate_data = {\n",
    "        x: mnist.validation.images,\n",
    "        y: mnist.validation.labels,\n",
    "    }\n",
    "    test_data = {x: mnist.test.images, y: mnist.test.labels}\n",
    "\n",
    "    for i in range(trainig_step):\n",
    "        xs, ys = mnist.train.next_batch(batch_size)\n",
    "        _, loss, logits22,pred2 = sess.run(\n",
    "            [optimizer, cross_entropy_loss, logits, pred],\n",
    "            feed_dict={\n",
    "                x: xs,\n",
    "                y: ys,\n",
    "                learning_rate: 0.001\n",
    "            })\n",
    "        #每500次训练打印一次损失值与验证准确率\n",
    "        if i > 0 and i % 500 == 0:\n",
    "            validate_accuracy = sess.run(accuracy, feed_dict=validate_data)\n",
    "            print(\n",
    "                \"after %d training steps, the loss is %g, the validation accuracy is %g\"\n",
    "                % (i, loss, validate_accuracy))\n",
    "            saver.save(sess, './model.ckpt', global_step=i)\n",
    "\n",
    "    print(\"the training is finish!\")\n",
    "    #最终的测试准确率\n",
    "    acc = sess.run(accuracy, feed_dict=test_data)\n",
    "    print(\"the test accuarcy is:\", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "INFO:tensorflow:Restoring parameters from ./model.ckpt-9500\n0.9845\n"
    },
    {
     "data": {
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      "text/plain": "<Figure size 576x576 with 16 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state('./')\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        final_pred, acc = sess.run(\n",
    "            [pred, accuracy],\n",
    "            feed_dict={\n",
    "                x: mnist.test.images,\n",
    "                y: mnist.test.labels\n",
    "            })\n",
    "        orders = np.argsort(final_pred)\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        print(acc)\n",
    "        for idx in range(16):\n",
    "            order = orders[idx, :][-1]\n",
    "            prob = final_pred[idx, :][order]\n",
    "            plt.subplot(4, 4, idx + 1)\n",
    "            plt.axis('off')\n",
    "            plt.title('{}: [{}]-[{:.1f}%]'.format(mnist.test.labels[idx],\n",
    "                                                  order, prob * 100))\n",
    "            plt.imshow(mnist.test.images[idx].reshape((28, 28)))\n",
    "\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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